Phase-Contrast MRI Phase-Error Correction
Volumetric phase-contrast magnetic resonance imaging (4DPC MRI) is a promising technique for obtaining data of periodic motion and blood flow. The usefulness of the technique has been limited, however, by the lack of methods for addressing phase-offset errors, presumed to be related to uncompensated eddy-currents during the MRI acquisition. Techniques for addressing phase-offset errors in volumetric data have relied on techniques directly adapted from planar, two-dimensional PC-MRI.
Two-dimensional phase-contrast MRI is a commonly-employed imaging method to obtain data for quantification of flow in blood vessels. In current clinical practice, this is performed with a two-dimensional planar acquisition oriented perpendicular to the axis of a vessel of interest. An image of the through-plane component of velocity is obtained over multiple phases of the cardiac cycle. To obtain volumetric flow measurements from this data, through-plane velocities are summed over a region bounded by the outline of the vessel of interest. These measurements are subject to phase-offset errors (1, 2), which are not easily corrected in this planar data.
Phase-offsets, presumed to be related to uncompensated eddy-currents, can confound measurements of blood flow. One method to correct for these errors is the manual subtraction of velocities from a selected area of stationary tissue in the slice plane. This method is the most widely used because it is the easiest to perform, but is limited because of (a) its need for manual intervention, and (b) it does not completely account for the spatial dependence of phase-error. Another proposed method to correct for these errors is the subtraction of errors measured in a stationary phantom (3). This second method however, is laborious and doubles the acquisition time on the MRI scanner, making it impractical for use in the clinical environment. A third proposed approach is to utilize automated computational procedures for phase-offset correction, the simplest of which utilizes a linear, two-dimensional model of the formf(x,y)=c0+cxx+cyy  (1)The parameters of this model (c0, cx, cy) are then estimated using data from static tissues in each plane of image data, typically with a least squares regression approach (4). The resulting phase-offset model f (x,y) is then subtracted from each pixel of the velocity image.
Other two-dimensional models have also been proposed. These are of the form
                                                        f                              n                ,                m                                      ⁡                          (                              x                ,                y                            )                                =                                    c              0                        +                                          ∑                                  i                  =                  1                                n                            ⁢                                                          ⁢                                                c                                      x                    ,                    i                                                  ⁢                                  x                  i                                                      +                                          ∑                                  i                  =                  1                                m                            ⁢                                                          ⁢                                                c                                      y                    ,                    i                                                  ⁢                                  y                  i                                                                    ,                            (        2        )            where n and m are non-negative integers. This family of models includes the linear model described above, when n and m are both set to 1, but also provides an opportunity to add higher order terms to model finer spatial variations. While higher-order terms are possible, an earlier study has suggested that modeling and subtracting these terms may not improve overall accuracy (5).
Volumetric phase-contrast MRI is a related MRI technique that acquires an entire volume of data instead of a single plane, resolving a three dimensional vector field, a vector encoding motion for each voxel. Each component of velocity is subject to eddy-current-related phase offsets that can be spatially dependent, and can therefore confound quantification of flow. The typical method for correcting this data is to once again use a model of the form,f(x,y)=c0+cx+cyy  (3)
This model can be separately applied to each flow direction, in each plane of data within the imaging volume, and typically is also applied separately to each temporal phase of data. This approach results in separate models for each slice of data for each gated time-point. We believe that modeling this data slice-by-slice and phase-by-phase may be unnecessarily and unpredictably inconsistent and therefore provide herein with this invention a new, more reliable method for performing correction of phase-offset errors.
Vector Field Fusion Visualization and Quantification
Volumetric phase-contrast magnetic resonance imaging is an evolving imaging technique that not only provides anatomic information about structures within the imaging volume, but also provides a vector field of data encoding motion. We recognize that this has considerable potential for evaluation of cardiac and cardiovascular diseases. Recent advances in MR imaging have now recently made it possible to acquire near-isotropic, high-resolution images while preserving high signal-to-noise of both the anatomic images and vector fields. However, no computer system yet exists that can enable clinical image interpretation and diagnosis, which is generally carried out by physicians with training in radiology and/or cardiology.
We have therefore devised a series of inventions and steps that facilitate image interpretation and quantitative analysis of 4DPC MRI data. To be used in clinical practice, we provide herein with this invention a series of inventions that allow analysis of the imaging volume, optimizing user-interaction by leveraging graphics hardware. This invention allows the user to dynamically perform the necessary visualization and computational tasks, making it feasible to perform cardiovascular examinations with the 4DPC imaging technique in the clinical environment.